Method and apparatus for reducing echo

ABSTRACT

The embodiments of the present disclosure provide a method and an apparatus for reducing an echo, which apply a first stage adaptive filter to a downlink reference signal to obtain a first stage filtered signal and a first error signal; after performing a K-path gain process and a K-path pre-distortion process to the downlink reference signal, apply the at least one second stage adaptive filter to a K-path pre-distorted signal to obtain a second stage filtered signal and a second error signal; and perform a minimum value fusion process to the first error signal and the second error signal so as to obtain a residue signal, which is considered as a final output of adaptive echo cancellation. Accordingly, the present disclosure realizes steadily providing a relatively high echo loss under a circumstance that a speaker suffers a relatively great distortion.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese patent applicationNo. 201410857171.6, filed on Dec. 30, 2014, and entitled “METHOD ANDAPPARATUS FOR REDUCING ECHO”, the entire disclosure of which isincorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to echo technology, and moreparticularly, to a method and an apparatus for reducing an echo.

BACKGROUND

In audio systems, due to existence of signal reflecting paths,interference of echoes is inevitable. Echoes in audio communicationsinclude electrical echoes and acoustic echoes, where electrical echoesare caused by signal energy reflection generated by impedance mismatchand acoustic echoes refer to voices from a speaker at a listening sideare collected by a microphone and transmitted back to a speaking side,where acoustic echoes include direct acoustic echoes and indirectacoustic echoes. Direct acoustic echoes are voices from a speakerdirectly collected by a microphone, and indirect acoustic echoes arevoices from a speaker collected by a microphone, where the voices passdifferent paths (e.g. a house or any objects in a house) and arereflected one or more times before being collected by the microphone.Echoes suffering channel latency are transmitted back to the speakingend and heard by a teller, so that causing interference on audios at thespeaking end, which reduces clarity of the audios and affects quality ofaudio communications.

In order to cancel impacts on audio communications by echoes, in 1960s,Sondhi from the Bell Laboratory firstly presented that applying adaptivefilters to realize cancellation of echoes. Referring to FIG. 1, a blockdiagram presenting a conventional system for adaptive echo cancellationis provided. Before a speaker 1 at a close end presents a downlinkreference signal that is from a remote end, the downlink referencesignal passes through an electrical echo path 2 and form an electricalecho; after the speaker 1 at the close end presents the downlinkreference signal, the downlink reference signal is received by amicrophone via an acoustic echo path 3 and form an acoustic echo. Anadaptive filter 4 performs filtering to the downlink reference signalusing an echo path model 5, and use a filtered output (namely, acancellation signal) to cancel out the acoustic echo so that obtaining aresidue signal (namely, an error signal), which is transmitted to theremote end. Simultaneously, an adaptive filtering algorithm 6 inside theadaptive filter 4 modifies parameters of the echo path model 5 based onthe downlink reference signal and the error signal, so as to attenuatethe remaining acoustic echo gradually.

In echo cancellation technologies, since an acoustic echo possessescharacteristics that multi-path, long latency, slow attenuation,time-varying, non-linearity, etc., the adaptive filter 4 with goodperformance for Acoustic Echo Cancellation (AEC) is needed; especially,for hand-held devices with relatively severe non-linearity, the adaptivefilter 4 with even better performance is required. Due tominiaturization of hand-held devices, comparing to regular speakers,micro speakers of the hand-held devices is much smaller in size. Inorder to achieve a required voice volume of hands-free communications,the micro speakers frequently operate in a non-linear domain, so thatdistortion becomes even more severe. Under this circumstance, theadaptive filter 4 provides a very unstable echo with very small loss,and the echo generally has no loss while facing a jump signal.Accordingly, providing a method and an apparatus for reducing an echo isrequired, which steadily provide a relatively high echo loss under acircumstance that a speaker suffers a relatively great distortion.

SUMMARY

Regarding to the problem addressed in the background, embodiments of thepresent disclosure provide a method and an apparatus for reducing anecho, which steadily provide a relatively high echo loss under acircumstance that a speaker suffers a relatively great distortion.

A method for reducing an echo, including:

invoking a first stage adaptive filter corresponding to a downlinkreference signal x(t), and performing a first filtering process to thedownlink reference signal x(t) so as to obtain a first stage filteredsignal y₀(t);

subtracting a target signal by the first stage filtered signal y₀(t) soas to obtain a first error signal e₀(t);

performing a K-path gain process to the downlink reference signal x(t)so as to obtain a K-path pre-processed signal, where K is a positiveinteger;

performing a pre-distortion process to the K-path pre-processed signalso as to obtain a corresponding K-path pre-distorted signal r_(k)(t)(k=1, 2, . . . , K);

invoking at least one second stage adaptive filter corresponding to theK-path pre-distorted signal, and performing a second filtering processto the K-path pre-distorted signal so as to obtain a correspondingK-path second stage filtered signal y_(k)(t);

subtracting the first error signal e₀(t) by the K-path second stagefiltered signal y_(k)(t) so as to obtain a second error signal e_(k)(t),where k=1, 2, . . . , K;

performing a minimum value fusion process to the first error signale₀(t) and the second error signal e_(k)(t) so as to obtain a residuesignal e(t); and

considering the residue signal e(t) as a final output of adaptive echocancellation.

In some embodiments, if the echo path model in the first stage adaptivefilter corresponding to the downlink reference signal x(t) is a timedomain model h_(0,t), the first error signal e₀(t) is expressed as:

${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$where d(t) is a target signal, y₀(t) is the first stage filtered signal,h_(0,t) is a M-order FIR filter at time t, h_(0,t)=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex and M is a number of order.

In some embodiments, if the echo path model is a time domain modelh_(0,t), the echo path model h_(0,t+1) of the first stage adaptivefilter corresponding to the downlink reference signal x(t) is updatedas:h _(0,t−1) =h _(0,t) +Δh _(0,t),where Δh_(0,t) that is a M-order vector with M a positive integer, is aterm for updating a parameter of the first stage adaptive filter in timedomain.

In some embodiments, if the echo path model in the first stage adaptivefilter corresponding to the downlink reference signal x(t) is afrequency domain model H_(0,t), the first error signal e₀(t) isexpressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(t)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)],where d(t) is the target signal, y₀(t) is the first stage filteredsignal, t is the time index, N is length of a signal frame, M is thenumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix,I_((N−M)×(N−M)) is a (N−M) by (N−M) identity matrix, F⁻ is an inversediscrete Fourier transform matrix, denotes a N-order vector at the timet, and R_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T).

In some embodiments, if the echo path model is a frequency domain modelH_(0,t), the echo path model H_(0,t+1) of the first stage adaptivefilter corresponding to the downlink reference signal x(t) is updatedas:H _(0,t+1) =H _(0,t) +ΔH _(0,t),where ΔH_(0,t) that is a N-order vector with N a positive integer, is aterm for updating a parameter of the first stage adaptive filter infrequency domain.

In some embodiments the pre-distortion mapping function employed by thepre-distortion process is expressed as:r _(k)(t)=f _(k)(p _(k)(t)),where r_(k)(t) is the k-th pre-distorted signal, p_(k)(t) is the k-thpre-processed signal, f_(k)(x)≠cx, f_(k)(x)≠c, c is a constant, and k=1,2, . . . , K.

In some embodiments, if an echo path model in the at least one secondstage adaptive filter corresponding to the K-path pre-distorted signalis a time domain model h_(k,t), the second error signal is expressed as:

${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$where h_(k,t) is a k-th M-order FIR filter at a time t, k=1, 2, . . . ,K, h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose,

denotes convolution, t is the time index and M is the number of order.

In some embodiments, if the echo path model is a time domain modelh_(k,t), the echo path model h_(k,t+1) of the at least one second stageadaptive filter corresponding to the K-path pre-distorted signal isupdated as:h _(k,t+1) =h _(k,t) +Δh _(k,t),where Δh_(k,t) with k=1, 2, . . . , K that is a M-order vector with M apositive integer, is the term for updating the parameter of the at leastone second stage adaptive filter in time domain.

In some embodiments, if the echo path model in the at least one secondstage adaptive filter corresponding to the K-path pre-distorted signalis a frequency domain model H_(k,t), the second error signal e_(k)(t) isexpressed as:e _(k)(t)=e ₀(t)−y _(k)(t)[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M)×(N−M))]F ⁻[H _(k,t) ·R _(k,t)],where y_(k)(t) is the second stage filtered signal, t is the time index,N is length of a signal frame, M is the number of order, 0_((N−M)×M) isthe (N−M) by M zero matrix, I_((N−M)×(N−M)) is the (N−M) by (N−M)identity matrix, F⁻ is the inverse discrete Fourier transform matrix,H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)=F[r_(k)(t−N+1), r_(k)(t−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. , K.

In some embodiments, if the echo path model is a frequency domain modelH_(k,t), the echo path model H_(k,t+1) of the at least one second stageadaptive filter corresponding to the K-path pre-distorted signal isupdated as:H _(k,t+1) =H _(k,t) +ΔH _(k,t),where ΔH_(k,t) that is a N-order vector with N a positive integer, is aterm for updating a parameter of the at least one second stage adaptivefilter in frequency domain.

In some embodiments, performing a minimum value fusion process to afirst error signal e₀(t) and a second error signal e_(k)(t) (k=1, 2, . .. , K) so as to obtain a residue signal, including:

mapping the first error signal e₀(t) and the second error signale_(k)(t) (k=1, 2, . . . , K) to corresponding mapping signalsrespectively using an invertible space mapping method;

computing metrics corresponding to the mapping signals using a presetminimum metric function;

searching for a minimum metric from the metrics; and

mapping a mapping signal corresponding to the minimum metric back to aspace in which the first error signal e₀(t) and the second error signale_(k)(t) reside, so as to obtain the residue signal e(t).

An apparatus for reducing an echo, including:

a first stage filtering unit, adapted for invoking a first stageadaptive filter corresponding to a downlink reference signal x(t), andperforming a first filtering process to the downlink reference signalx(t) so as to obtain a first stage filtered signal y₀(t);

a first subtracting unit, adapted for subtracting a target signal by thefirst stage filtered signal y₀(t) so as to obtain a first error signale₀(t);

a gain unit, adapted for performing a K-path gain process to thedownlink reference signal x(t) so as to obtain a K-path pre-processedsignal, where K is a positive integer;

a pre-distortion processing unit, adapted for performing apre-distortion process to the K-path pre-processed signal so as toobtain a corresponding K-path pre-distorted signal r_(k)(t) (k=1, 2, . .. , K);

a second stage filtering unit, adapted for invoking at least one secondstage adaptive filter corresponding to the K-path pre-distorted signal,and performing a second filtering process to the K-path pre-distortedsignal so as to obtain a corresponding K-path second stage filteredsignal y_(k)(t);

a second subtracting unit, adapted for subtracting the first errorsignal e₀(t) by the K-path second stage filtered signal y_(k)(t) so asto obtain a second error signal e_(k)(t), where k=1, 2, . . . , K;

a fusion processing unit, adapted for performing a minimum value fusionprocess to the first error signal e₀(t) and the second error signale_(k)(t) so as to obtain a residue signal e(t); and

an output unit, adapted for considering the residue signal e(t) as afinal output of adaptive echo cancellation.

In some embodiments, if the echo path model in the first stage adaptivefilter corresponding to the downlink reference signal x(t) is a timedomain model h_(0,t), the first error signal e₀(t) is expressed as:

${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$where d(t) is a target signal, y₀(t) is the first stage filtered signal,h_(0,t) is a M-order FIR filter at time t, h_(0,t)=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex and M is a number of order.

In some embodiments, if the echo path model is a time domain modelh_(0,t), the echo path model h_(0,t+1) of the first stage adaptivefilter corresponding to the downlink reference signal x(t) is updatedas:h _(0,t+1) =h _(0,t) +Δh _(0,t),where Δh_(0,t) that is a M-order vector with M a positive integer, is aterm for updating a parameter of the first stage adaptive filter in timedomain.

In some embodiments, if the echo path model in the first stage adaptivefilter corresponding to the downlink reference signal x(t) is afrequency domain model H_(0,t), the first error signal e₀(t) isexpressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(t)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)],where d(t) is the target signal, y₀(t) is the first stage filteredsignal, t is the time index, N is length of a signal frame, M is thenumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix,I_((N−M)×(N−M)) is a (N−M) by (N−M) identity matrix, F⁻ is an inversediscrete Fourier transform matrix, H_(0,t) denotes a N-order vector atthe time t, and R_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T).

In some embodiments, if the echo path model is a frequency domain modelH_(0,t), the echo path model H_(0,t+1) of the first stage adaptivefilter corresponding to the downlink reference signal x(t) is updatedas:H _(0,t+1) =H _(0,t) +ΔH _(0,t),where ΔH_(0,t) that is a N-order vector with N a positive integer, is aterm for updating a parameter of the first stage adaptive filter infrequency domain.

In some embodiments, the pre-distortion mapping function employed by thepre-distortion process is expressed as:r _(k)(t)=f _(k)(p _(k)(t)),where r_(k)(t) is the k-th pre-distorted signal, p_(k)(t) is the k-thpre-processed signal, f_(k)(x)≠cx, f_(k)(x)≠c, c is a constant, and k=1,2, . . . , K.

In some embodiments, if an echo path model in the at least one secondstage adaptive filter corresponding to the K-path pre-distorted signalis a time domain model h_(k,t), the second error signal is expressed as:

${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$where is a k-th M-order FIR filter at a time t, k=1, 2, . . . , K,h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose,

denotes convolution, t is the time index and M is the number of order.

In some embodiments, if the echo path model is a time domain modelh_(k,t), the echo path model h_(k,t+1) of the at least one second stageadaptive filter corresponding to the K-path pre-distorted signal isupdated as:h _(k,t+1) =h _(k,t) +Δh _(k,t),where Δh_(k,t) with k=1, 2, . . . , K that is a M-order vector with M apositive integer, is the term for updating the parameter of the at leastone second stage adaptive filter in time domain.

In some embodiments, if the echo path model in the at least one secondstage adaptive filter corresponding to the K-path pre-distorted signalis a frequency domain model H_(k,t), the second error signal e_(k)(t) isexpressed as:e _(k)(t)=e ₀(t)−y _(k)(t),[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M))×(N−M)]F ⁻[H _(k,t) ·R _(k,t)],where y_(k)(t) is the second stage filtered signal, t is the time index,N is length of a signal frame, M is the number of order, 0_((N−M)×M) isthe (N−M) by M zero matrix, I_((N−M)×(N−M)) is the (N−M) by (N−M)identity matrix, F⁻ is the inverse discrete Fourier transform matrix,H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)=F[r_(k)(t−N+1), r_(k)(−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. , K.

In some embodiments, if the echo path model is a frequency domain modelH_(k,t), the echo path model H_(k,t+1) of the at least one second stageadaptive filter corresponding to the K-path pre-distorted signal isupdated as:H _(k,t+1) =H _(k,t) +ΔH _(k,t),where ΔH_(k,t) that is a N-order vector with N a positive integer, is aterm for updating a parameter of the at least one second stage adaptivefilter in frequency domain.

In some embodiments, the fusion processing unit may include:

a mapping sub-unit, adapted for mapping the first error signal e₀(t) andthe second error signal e_(k)(t) (k=1, 2, . . . , K) to correspondingmapping signals respectively using an invertible space mapping method;

a metric computing sub-unit, adapted for computing metrics correspondingto the mapping signals using a preset minimum metric function;

a searching sub-unit, adapted for searching for a minimum metric fromthe metrics; and

a residue signal obtaining sub-unit, adapted for mapping a mappingsignal corresponding to the minimum metric back to a space in which thefirst error signal e₀(t) and the second error signal e_(k)(t) reside, soas to obtain the residue signal e(t).

From above, the embodiments of the present disclosure provide a methodand an apparatus, which employ the first stage adaptive filtercorresponding to the downlink reference signal to perform a first stageecho cancellation to an output of echo cancellation so that the firsterror signal may be obtained; after performing the K-path gain processand the K-path pre-distortion process to the downlink reference signal,employ the at least one second stage adaptive filter corresponding tothe K-path pre-distorted signal to perform a second stage echocancellation to the output of echo cancellation so that the second errorsignal may be obtained; then, perform the minimum value fusion processto an output of the first stage echo cancellation and an output of thesecond stage echo cancellation so as to obtain the residue signal, andconsider the residue signal as the final output of adaptive echocancellation. The embodiments of the present disclosure may employ amethod for reducing in cascade an echo to minimize the residue signalobtained finally, which means an echo loss may be relatively large, andthus, the present disclosure realizes steadily providing a relativelyhigh echo loss under a circumstance that a speaker suffers a relativelygreat distortion.

BRIEF DESCRIPTION OF THE DRAWINGS

For better clarifying embodiments of the present disclosure or priorart, a brief description of drawings needed for the description of theembodiments and prior art is provided. Obviously, the drawings whichfollow are the embodiments of the present disclosure. By taking effortwith creativity, those skilled in the art can acquire other drawingsbased on the drawings provided.

FIG. 1 schematically illustrates a block diagram presenting anconventional adaptive echo cancellation system;

FIG. 2 schematically illustrates a flow diagram presenting a method forreducing an echo according to one embodiment in the present disclosure;

FIG. 3 schematically illustrates a flow diagram presenting a method forperforming a minimum value fusion process to a first error signal and asecond error signal so as to obtain a residue signal according to oneembodiment in the present disclosure;

FIG. 4 schematically illustrates a structural diagram presenting anapparatus for reducing an echo according to one embodiment in thepresent disclosure; and

FIG. 5 schematically illustrates a structural diagram presenting afusion process unit according to one embodiment in the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure provide a method and an apparatusfor reducing an echo so as to realize steadily providing a relativelyhigh echo loss under a circumstance that a speaker suffers a relativelygreat distortion.

Referring to FIG. 2, a flow diagram presenting a method for reducing anecho according to one embodiment of the present disclosure is provided,and the method includes:

S11: invoking a first stage adaptive filter corresponding to a downlinkreference signal x(t), and performing a first filtering process to thedownlink reference signal x(t) so as to obtain a first stage filteredsignal y₀(t);

S12: subtracting a target signal by the first stage filtered signaly₀(t) so as to obtain a first error signal e₀(t), where subtracting thetarget signal by the first stage filtered signal is for cancelling alinear echo in the target signal;

S13: performing a K-path gain process to the downlink reference signalx(t) so as to obtain a K-path pre-processed signal, where K is apositive integer; the K-path gain process may include: multiplying thedownlink reference signal x(t) by a gain g_(k)(k=1, 2, . . . , K) so asto obtain a K-path pre-processed signal p_(k)(t) (k=1, 2, . . . , K),and corresponding formulas are presented in the following:

$\begin{matrix}{{{p_{1}(t)} = {g_{1}x(t)}},} \\{{{p_{2}(t)} = {g_{2}x(t)}},} \\\vdots \\{{{p_{K}(t)} = {g_{K}x(t)}},}\end{matrix}$where the gains 0≦g_(k)≦1 for k=1, 2, . . . , K; specifically, the gainis selected not greater than 1 so as to avoid that the downlinkreference signal generates an additional overflow distortion in adigital system;

S14: performing a pre-distortion process to the K-path pre-processedsignal so as to obtain a corresponding K-path pre-distorted signalr_(k)(t) (k=1, 2, . . . , K);

S15: invoking at least one second stage adaptive filter corresponding tothe K-path pre-distorted signal, and performing a second filteringprocess to the K-path pre-distorted signal so as to obtain acorresponding K-path second stage filtered signal y_(k)(t);

S16: subtracting the first error signal e₀(t) by the K-path second stagefiltered signal y_(k)(t) so as to obtain a second error signal e_(k)(t)(k=1, 2, . . . , K);

S17: performing a minimum value fusion process to the first error signale₀(t) and the second error signal e_(k)(t) so as to obtain a residuesignal e(t);

S18: considering the residue signal e(t) as a final output of adaptiveecho cancellation.

From above, the first stage adaptive filter corresponding to thedownlink reference signal may perform a first stage echo cancellation toan output of echo cancellation so that the first error signal may beobtained; after performing the K-path gain process and the K-pathpre-distortion process to the downlink reference signal, the at leastone second stage adaptive filter corresponding to the K-pathpre-distorted signal may perform a second stage echo cancellation to theoutput of echo cancellation so that the second error signal may beobtained; then, the minimum value fusion process is performed to anoutput of the first stage echo cancellation and an output of the secondstage echo cancellation so as to obtain the residue signal, and theresidue signal is considered as the final output of adaptive echocancellation. The embodiments of the present disclosure may employ amethod for reducing in cascade an echo to minimize the residue signalobtained finally, which means an echo loss may be relatively large, andthus, the embodiments of the present disclosure realizes steadilyproviding a relatively high echo loss under a circumstance that aspeaker suffers a relatively great distortion.

Specifically, an echo path model in an adaptive filter is either a timedomain model or a frequency domain model. Regarding to these two models,the embodiments of the present disclosure provide following descriptionsabout the first stage adaptive filter corresponding to the downlinkreference signal x(t) and the first error signal e₀(t).

(1) if the echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a time domainmodel h_(0,t), the first error signal e₀(t) is expressed as:

${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$where d(t) is a target signal, y₀(t) is the first stage filtered signal,h_(0,t) is a M-order FIR filter at time t, h_(0,t)=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex and M is a number of order; the order M may need to satisfy thesimulated echo path model; generally, echo attenuation may last 10 ms to1 s so that the order M ranges from 0.01 f_(s) to f_(s), where f, is asampling frequency.

If the echo path model is the time domain model h_(0,t), the first stageadaptive filter may employ any one from the time domain adaptivefiltering algorithms, where the time domain adaptive filteringalgorithms include: the Least Mean Square (LMS), the Normalized LeastMean Square (NMLS), the Affine Projection (AP), the Fast AffineProjection (FAP), the Least Square (LS), the Recursive Least Square(RLS), etc.

An embodiment of the present disclosure uses NMLS:

Δ h_(0, t) = μ_(h, 0)[Δ h_(0, t)(1), Δ h_(0, t)(2), …  , Δ h_(0, t)(M)]^(T)${{\Delta\;{h_{0,t}(m)}} = \frac{{e_{0}(t)}{x( {t - M + m} )}}{ɛ + {\sum\limits_{m = 1}^{M}{x( {t - M + m} )}^{2}}}},$where ε is a small positive real number used for avoiding diving byzero, μ_(k,0) is a step length of update, 0<μ_(h,0)<2, the superscript Tdenotes transpose, M is the number of order, and t is the time index.

The echo path model h_(0,t+1) of the first stage adaptive filtercorresponding to the downlink reference signal x(t) is updated as:h_(0,t+1)=h_(0,t)+Δh_(0,t).

(2) if the echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a frequencydomain model H_(0,t), the first error signal e₀(t) is expressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(T)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)],where d(t) is the target signal, y₀(t) is the first stage filteredsignal, t is the time index, N is length of a signal frame, M is thenumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix,I_((N−M)×(N−M)) is a (N−M) by (N−M) identity matrix, F⁻ is an inversediscrete Fourier transform matrix, denotes a N-order vector at the timet, and R_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T), · denotes dotproduct, F is a discrete Fourier transform matrix, and the superscript Tdenotes transpose.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency,and N may need to be greater than M.

If the echo path model is the frequency domain model H_(0,t), the firststage adaptive filter may employ any one from the frequency domainadaptive filtering algorithms, where the frequency domain adaptivefiltering algorithms include: the Frequency Domain Adaptive Filter(FDAF), the Multi-Delay Adaptive Filter (MDAF), the Windowing FrequencyDomain Adaptive Filter (WDAF), etc. A term for updating a parameter ofthe first stage adaptive filter in frequency domain is ΔH_(0,t), whereΔH_(0,t) is a N-order vector with N a positive integer.

An embodiment of the present disclosure uses FDAF:

${{\Delta\; H_{0,t}} = {\mu_{H,0}\frac{{R_{0,t}}^{*}E_{0,t}}{ɛ + {E\lbrack {R_{0,t}}^{2} \rbrack}}}},$where ε is a small positive real number used for avoiding diving byzero, a superscript * denotes conjugate, μ_(H,0) is a step length ofupdate, 0<μ_(H.0)<2,E _(0,t) =F[e ₀(t−N+1),e ₀(t−N+2), . . . ,e ₀(t)]^(T),R _(0,t) =F[x(t−N+1),x(t−N+2), . . . ,x(t)]^(T),E[|R_(0,t)|²] is an expectation of an energy spectrum of R_(0,t), whichis generally obtained by employing the regression method that is:E[|R _(0,t)|²]=ηE[|R_(0,t−1)|²]+(1−η)|R _(0,t)|²,where η is an updating factor satisfying 0<η<1.

The echo path model H_(0,t+1) of the first stage adaptive filtercorresponding to the downlink reference signal x(t) is updated as:H_(0,t+1)=H_(0,t)+ΔH_(0,t), where ΔH_(0,t) is a N-order vector with N apositive integer.

In order to further improve the aforementioned embodiment, the S14specifically is: a K-path pre-distortion mapping function may map theK-path pre-processed signal p_(k)(t) into the K-path pre-distortedsignal r_(k)(t), where k=1, 2, . . . , K.

The K-path pre-distortion mapping function employed by thepre-distortion process is expressed as: r_(k)(t)=f_(k)(p_(k)(t)), wherer_(k)(t) is the k-th pre-distorted signal, p_(k)(t) is the k-thpre-processed signal, f_(k)(x)≠cx, f_(k)(x)≠c, c is a constant, and k=1,2, . . . , K.

For convenience of designing and using the pre-distortion mappingfunction, normalizing the pre-distortion mapping function as:

${{r_{k}(t)} = {x_{\max}{f_{k}( \frac{p_{k}(t)}{x_{\max}} )}}},$where x_(max) is a maximum amplitude of the downlink reference signalx(t), −1≦f_(k)(x)≦1, k=1, 2, . . . , K.

There are many pre-distortion mapping functions, the formulas which arecommon but not limited to, are listed in the following:f _(k)(x)=|x| ^(γ) +c,f _(k)(x)=sign(x)|x| ^(γ) +c,f _(k)(x)=sin(cx),f _(k)(x)=tan(cx),and combinations of them, such as:f _(k)(x)=a ₁ |x| ^(γ) ¹ +a ₂sign(x)|x| ^(γ) ² +a ₃ sin(c ₃ x)+a ₄ tan(c₄ x)+c.

The pre-distortion mapping function may be a piecewise function, suchas:

${f_{k}(x)} = \{ {\begin{matrix}{x + c_{1}} & {{x} < x_{1}} \\{{{{sign}(x)}{x}^{\gamma}} + c_{2}} & {x_{1} \leq {x} < x_{2}} \\c_{3} & {x_{2} \leq {x}}\end{matrix},} $where c, c₁, c₂, c₃, c₄, γ, γ₁, γ₂, a₁, a₂, a₃, a₄, x₁, x₂ are allconstant real numbers, and sign( ) denotes a sign function.

A reason for multiple pre-distortion mapping functions are needed forobtaining the pre-distorted signal is that distortion of a speakerpossesses features of complexity and time-variance, and it is unlikelyfor one distortion process to effectively approach a distortion portionin an echo signal, so that the embodiments of the present disclosureemploy results of different multi-path distortion processes forproviding a plenty of selections to a final minimum fusion.

Specifically, an echo path model in an adaptive filter is either a timedomain model or a frequency domain model. Regarding to these two models,the embodiments of the present disclosure provide following descriptionsabout the at least one second stage adaptive filter corresponding to theK-path pre-distorted signal and the second error signal e_(k)(t).

(1) if an echo path model in the at least one second stage adaptivefilter corresponding to the K-path pre-distorted signal is a time domainmodel h_(k,t), the second error signal e_(k)(t) is expressed as:

${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$where h_(k,t) is a k-th M-order FIR filter at a time t,h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose,

denotes convolution, t is the time index and M is the number of order.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency.

If the echo path model is the time domain model h_(k,t)(k=1, 2, . . . ,K), the at least one second stage adaptive filter may employ any onefrom the time domain adaptive filtering algorithms, where the timedomain adaptive filtering algorithms include: the Least Mean Square(LMS), the Normalized Least Mean Square (NMLS), the Affine Projection(AP), the Fast Affine Projection (FAP), the Least Square (LS), theRecursive Least Square (RLS), etc. A term for updating a parameter ofthe at least one second stage adaptive filter in time domain isΔh_(k,t)(k=1, 2, . . . , K), where Δh_(k,t) is a M-order vector with M apositive integer.

An embodiment of the present disclosure employs NMLS:

Δ h_(k, t) = μ_(h, k)[Δ h_(k, t)(1), Δ h_(k, t)(2), …  , Δ h_(k, t)(M)]^(T)${{\Delta\;{h_{k,t}(m)}} = \frac{{e_{k}(t)}{r_{k}( {t - M + m} )}}{ɛ + {\sum\limits_{m = 1}^{M}{r_{k}( {t - M + m} )}^{2}}}},$where ε is a small positive real number used for avoiding diving byzero, μ_(h,0) and μ_(h,k) are the step length of update, 0<μ_(h,0)<2,0<μ_(h,k)<2, k=1, 2, . . . , K, and t is the time index.

The echo path model h_(k,t+1) of the at least one second stage adaptivefilter corresponding to the pre-distorted signal is updated as:h_(k,t+1)=h_(k,t)+Δh_(k,t), where the term for updating the parameter ofthe at least one second stage adaptive filter in time domain isΔh_(k,t), where Δh_(k,t) is a M-order vector, M is a positive integer,and k=1, 2, . . . , K.

(2) if the echo path model in the at least one second stage adaptivefilter corresponding to the pre-distorted signal is a frequency domainmodel H_(k,t), the second error signal e_(k)(t) is expressed as:e _(k)(t)=e ₀(t)−y _(k)(t),[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M)×(N−M))]F ⁻[H _(k,t) ·R _(k,t)],where y_(k)(t) is the second stage filtered signal, t is the time index,N is length of a signal frame, M is the number of order, 0_((N−M)×M) isthe (N−M) by M zero matrix, I_((N−M)×(N−M)) is the (N−M) by (N−M)identity matrix, F is the inverse discrete Fourier transform matrix,H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)=F[r_(k)(t−N+1), r_(k)(t−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. K, y₁(t), y₂(t), . . . , y_(K)(t) are K-path filtered output signals,· denotes the dot product, F is the discrete Fourier transform matrix,and the superscript T denotes transpose, k=1, 2, . . . K, and K is apositive integer.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency,and N may need to be greater than M.

If the echo path model is the frequency domain model H_(k,t), the atleast one second stage adaptive filter may employ any one from thefrequency domain adaptive filtering algorithms, where the frequencydomain adaptive filtering algorithms include: the Frequency DomainAdaptive Filter (FDAF), the Multi-Delay Adaptive Filter (MDAF), theWindowing Frequency Domain Adaptive Filter (WDAF), etc. A term forupdating a parameter of the at least one second stage adaptive filter infrequency domain is ΔH_(k,t), where ΔH_(k,t) is a N-order vector with Na positive integer.

An embodiment of the present disclosure employs FDAF:

${{\Delta\; H_{k,t}} = {\mu_{H,k}\frac{R_{k,t}{{}_{}^{}{}_{k,t}^{}}}{ɛ + {E\lbrack {R_{k,t}}^{2} \rbrack}}}},$where ε is a small positive real number used for avoiding diving byzero, the superscript * denotes conjugate, μ_(H,k) is a step length ofupdate, 0<μ_(H,k)<2 for k=1, 2, . . . , K,E _(k,t) =F[e_(k)(t−N+1),e _(k)(t−N+2), . . . ,e _(k)(t)]^(T),R _(k,t) =F[r_(k)(t−N+1),r _(k)(t−N+2), . . . ,r _(k)(t)]^(T),E[|R_(k,t)|²] is an expectation of an energy spectrum of R_(k,t), whichis generally obtained by employing the regression method that is:E[|R_(k,t)|²]=ηE[|R_(k,t−1)|²]+(1−η)|R _(k,t)|²,where η is a updating factor satisfying 0<η<1.

The echo path model H_(k,t+1) of the at least one second stage adaptivefilter corresponding to the K-path pre-distorted signal is updated as:H_(k,t+1)=H_(k,t)+ΔH_(k,t), where ΔH_(k,t) is a N-order vector with N apositive integer.

In order to further improve the aforementioned embodiment, referring toFIG. 3, the embodiment of the present disclosure provide a flow diagrampresenting a method for performing a minimum value fusion process to thefirst error signal and the second error signal so as to obtain a residuesignal, where the S17 may include: S21: mapping the first error signale₀(t) and the second error signal e_(k)(t) (k=1, 2, . . . , K) tocorresponding mapping signals respectively using an invertible spacemapping method; S22: computing metrics corresponding to the mappingsignals using a preset minimum metric function; S23: searching for aminimum metric from the metrics; S24: mapping a mapping signalcorresponding to the minimum metric back to a space in which the firsterror signal e₀(t) and the second error signal e_(k)(t) reside, so as toobtain the residue signal e(t).

Regarding to K+1 error signals (including the first error signal e₀(t)and the second error signal e_(k)(t) for k=1, 2, . . . , K, due toadaptive filters have different parameter signals, respective remainingechoes are minimum at different time or in different space. The minimumvalue fusion process may use a spatial mapping method to map the K+1error signals e₀(t), e₁(t), . . . , e_(K)(t) to mapping signals S_(0,t),S_(1,t), . . . , S_(K,1), and the preset minimum metric function may beused to compute the metrics v₀, v₁, . . . , v_(K) corresponding to themapping signals S_(0,t), S_(1,t), . . . , S_(K,1). A minimum metricv_(k) _(_) _(min) is searched among the metrics, and a k_min-th mappingsignal S_(k) _(_) _(min,t) corresponding to the minimum metric v_(k)_(_) _(min) is mapped back to an original space, in which the K+1 errorsignals reside, so as to obtain the residue signal e(t). Eventually, theresidue signal e(t) is regarded as the final output of adaptive echocancellation.

In a simplest minimum value fusion process, the spatial mapping methodis short-time signal framing that:S _(k,t)[e _(k)(t−L+1),e _(k)(t−L+2), . . . ,e _(k)(t)],k=0,1,2, . . .,K,where S_(k,t) is the mapping signal, and t is the time index.

A minimum metric function is used for computing a short-time extentthat:

${v_{k} = {{f_{m\; i\; n}( S_{k,t} )} = {\sum\limits_{l = 1}^{L}{{e_{k}( {t - L + l} )}}}}},{k = 0},1,2,\ldots\mspace{14mu},K,$where v_(k) is the minimum metric, and t is the time index.

Alternatively, a minimum metric function is used for computing ashort-time energy that:

${v_{k} = {{f_{m\; i\; n}( S_{k,t} )} = {\sum\limits_{l = 1}^{L}{e_{k}( {t - L + l} )}^{2}}}},{k = 0},1,2,\ldots\mspace{14mu},K,$where v_(k) is the minimum metric, and t is the time index.

In the aforementioned equations, L is expressed as a short-timeinterval, which is a positive integer, and a value of L is between 0.001f_(s) and f_(s), where f_(s) is the sampling frequency.

The mapping signal S_(k) _(_) _(min,t) corresponding to the minimumshort-time extent or the minimum short-time energy is selected, and aset [e_(k) _(_) _(min)(t−L+1), . . . , e_(k) _(_) _(min)(t)]corresponding to the mapping signal S_(k) _(_) _(min,t) is regards as aset of final residue signals [e(t−L+1), . . . , e(t)].

In some embodiments, the short-time interval may be overlapped so as toperform smoothing process to both ends of the short-time interval.

A more effective minimum fusion process may include: a frequency-domaintransform as presented in the following that:S_(k,t)=T_(F)([e_(k)(t−L+1), . . . , e_(k)(t)]), k=0, 1, 2, . . . , K,where S_(k,t) is the mapping signal, T_(F) denotes a frequency-domaintransform, L denotes the short-time interval, which is a positiveinteger, and the value of L is between 0.001 f_(s) and f_(s), wheref_(s) is the sampling frequency.

The frequency-domain transform T_(F) may include but be not limited thatthe Discrete Fourier Transform (DFT), the Discrete Cosine Transform(DCT), the Karhunen-Loeve (KL) transform, the Modified Discrete CosineTransform (MDCT), etc. The frequency-domain transform T_(F) may beinvertible and an inverse transform is expressed as T_(F) ⁻.

The mapping signal S_(k,t) obtained by the frequency-domain transform isa vector of L_(F) elements. For different mapping methods, L_(F) may bedifferent. In cases of the DFT transform, the DCT transform and the KLtransform, L_(F) is generally equal to L, and in a case of the MDCTtransform, L_(F) is equal to L/2. The minimum metric function may be anorm of the mapping signal S_(k,t)[l], l=1, 2, . . . , L_(F):f_(min)(x)=|x|, and alternatively, an addition of a weighted absolutevalue of a real part of a number and a weighted absolute value of animaginary part of the number:f _(min)(x)=λ_(real)|(real(x)|^(γ) ^(real) +λ_(imag)|imag(x)|^(γ)^(imag) ,where λ_(real) and λ_(imag) are weighting factors, which arenon-negative real numbers, and γ_(real) and γ_(imag) are order numbers,which are non-negative real numbers.

A metric of the mapping signal S_(k,t)[l], l=1, 2, . . . , L_(F) isobtained using the minimum metric function that:v_(k,t)=f_(min)(S_(k,t)[l]), where v_(k,l) is a minimum metric, aninteger index l=1, 2, . . . , L_(F) and k=0, 1, 2, . . . , K.

Based on the metric, the mapping signal S_(k,l)[l], l=1, 2, . . . ,L_(F) is fused as a fused signal S_(t)[l]=S_(k) _(_) _(l,t)[l], where aninteger index l=1, 2, . . . , L_(F), and k_l satisfies the followingequation that: f_(min)(S_(k) _(l) _(t)[l])=min([v_(0,l), v_(1,l), . . ., v_(K,l)]); at last, performing the inverse frequency-domain transformT_(F) ⁻, the fused signal S_(t)[l], l=1, 2, . . . , L_(F) is inverselymapped to obtain the set of final residue signals that [e(t−L+1), . . ., e(t)], where [e(t−L+1), . . . , e(t)]=T_(F) ⁻(S_(t)).

In some embodiments, the short-time interval may be overlapped so as toperform smoothing process to both ends of the short-time interval.

Specifically, in the aforementioned embodiments of the presentdisclosure, K is always a positive integer.

Further, in order to prove that the embodiments of the presentdisclosure realize steadily providing a relatively high echo loss undera circumstance that a speaker suffers a relatively great distortion,practical tests are performed.

A signal sampling frequency is 8000 Hz. The first stage adaptive filterand the at least one second stage adaptive filter both are FDAF. For thesecond stage, a two-path pre-distortion process is performed, where thegain for each path is 1, and the pre-distortion mapping functions:f₁(x)=sign(x)|x|^(0.1), f₂(x)=sign(x)|x|^(0.2).

The spatial mapping in the minimum fusion process employs DCT mapping,L=320, M=192, N=512, the minimum metric function is obtained by takingabsolute value.

Comparing processing results using the method provided in the presentdisclosure with processing results using a conventional method, it isnoted that a signal processed using the method provided in the presentdisclosure is smaller than a signal processed using the conventionalmethod, and the present disclosure achieves an improvement of more than4.2 dB.

Corresponding to the aforementioned embodiments of the method forreducing an echo, the embodiments of the present disclosure furtherprovide an apparatus for reducing an echo.

Referring to FIG. 4, a structural diagram presenting an apparatus forreducing an echo according to one embodiment of the present disclosureis provided, and the apparatus includes: a first stage filtering unit41, adapted for invoking a first stage adaptive filter corresponding toa downlink reference signal x(t), and performing a first filteringprocess to the downlink reference signal x(t) so as to obtain a firststage filtered signal y₀(t); a first subtracting unit 42, adapted forsubtracting a target signal by the first stage filtered signal y₀(t) soas to obtain a first error signal e₀(t), where subtracting the targetsignal by the first stage filtered signal is for cancelling a linearecho in the target signal; a gain unit 43, adapted for performing aK-path gain process to the downlink reference signal x(t) so as toobtain a K-path pre-processed signal, where K is a positive integer; theK-path gain process may include: multiplying the downlink referencesignal x(t) by a gain g_(k)(k=1, 2, . . . , K) so as to obtain a K-pathpre-processed signal p_(k)(t) (k=1, 2, . . . , K), and correspondingformulas are presented in the following:

$\begin{matrix}{{{p_{1}(t)} = {g_{1}x(t)}},} \\{{{p_{2}(t)} = {g_{2}x(t)}},} \\\vdots \\{{{p_{K}(t)} = {g_{K}x(t)}},}\end{matrix}$where the gains 0≦g_(k)≦1 for k=1, 2, . . . , K; specifically, the gainis selected not greater than 1 so as to avoid that the downlinkreference signal generates an additional overflow distortion in adigital system; a pre-distortion processing unit 44, adapted forperforming a pre-distortion process to the K-path pre-processed signalso as to obtain a corresponding K-path pre-distorted signal r_(k)(t)(k=1, 2, . . . , K); a second stage filtering unit 45, adapted forinvoking at least one second stage adaptive filter corresponding to theK-path pre-distorted signal, and performing a second filtering processto the K-path pre-distorted signal so as to obtain a correspondingK-path second stage filtered signal y_(k)(t); a second subtracting unit46, adapted for subtracting the first error signal e₀(t) by the K-pathsecond stage filtered signal y_(k)(t) so as to obtain a second errorsignal e_(k)(t) (k=1, 2, . . . , K); a fusion processing unit 47,adapted for performing a minimum value fusion process to the first errorsignal e₀(t) and the second error signal e_(k)(t) so as to obtain aresidue signal e(t); an output unit 48, adapted for considering theresidue signal e(t) as a final output of adaptive echo cancellation.

From above, the first stage adaptive filter corresponding to thedownlink reference signal may perform a first stage echo cancellation toan output of echo cancellation so that the first error signal may beobtained; after performing the K-path gain process and the K-pathpre-distortion process to the downlink reference signal, the at leastone second stage adaptive filter corresponding to the K-pathpre-distorted signal may perform a second stage echo cancellation to theoutput of echo cancellation so that the second error signal may beobtained; then, the minimum value fusion process is performed to anoutput of the first stage echo cancellation and an output of the secondstage echo cancellation so as to obtain the residue signal, and theresidue signal is considered as the final output of adaptive echocancellation. The embodiments of the present disclosure may employ amethod for reducing in cascade an echo to minimize the residue signalobtained finally, which means an echo loss may be relatively large, andthus, the present disclosure realizes steadily providing a relativelyhigh echo loss under a circumstance that a speaker suffers a relativelygreat distortion.

Specifically, an echo path model in an adaptive filter is either a timedomain model or a frequency domain model. Regarding to these two models,the embodiments of the present disclosure provide following descriptionsabout the first stage adaptive filter corresponding to the downlinkreference signal x(t) and the first error signal e₀(t).

(1) if the echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a time domainmodel h_(0,t) the first error signal e₀(t) is expressed as:

${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$where d(t) is a target signal, y₀(t) is the first stage filtered signal,h_(0,t) is an M-order FIR filter at time t, h_(0,t)=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex and M is a number of order.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is a sampling frequency.

If the echo path model is the time domain model h_(0,t), the first stageadaptive filter may employ any one from the time domain adaptivefiltering algorithms, where the time domain adaptive filteringalgorithms include: the Least Mean Square (LMS), the Normalized LeastMean Square (NMLS), the Affine Projection (AP), the Fast AffineProjection (FAP), the Least Square (LS), the Recursive Least Square(RLS), etc. A term for updating a parameter of the first stage adaptivefilter in time domain is Δh_(0,t), where Δh_(0,t) is a M-order vectorwith M a positive integer.

An embodiment of the present disclosure employs NMLS:

Δ h_(0, t) = μ_(h, 0)[Δ h_(0, t)(1), Δ h_(0, t)(2), …  , Δ h_(0, t)(M)]^(T)${{\Delta\;{h_{0,t}(m)}} = \frac{{e_{0}(t)}{x( {t - M + m} )}}{ɛ + {\sum\limits_{m = 1}^{M}{x( {t - M + m} )}^{2}}}},$where ε is a small positive real number used for avoiding diving byzero, μ_(h,0) is a step length of update, 0<μ_(h,0)<2, the superscript Tdenotes transpose, M is the number of order, and t is the time index.

The echo path model h_(0,t+1) of the first stage adaptive filtercorresponding to the downlink reference signal x(t) is updated as:h_(0,t+1)=h_(0,t)+Δh_(0,t).

(2) if the echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a frequencydomain model H_(0,t), the first error signal e₀(t) is expressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(t)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)],where d(t) is the target signal, y₀(t) is the first stage filteredsignal, t is the time index, N is length of a signal frame, M is thenumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix,I_((N−M)×(N−M)) is a (N−M) by (N−M) identity matrix, F⁻ is an inversediscrete Fourier transform matrix, denotes a N-order vector at the timet, and R_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T), · denotes dotproduct, F is a discrete Fourier transform matrix, and the superscript Tdenotes transpose.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency,and N may need to be greater than M.

If the echo path model is the frequency domain model H_(0,t), the firststage adaptive filter may employ any one from the frequency domainadaptive filtering algorithms, where the frequency domain adaptivefiltering algorithms include: the Frequency Domain Adaptive Filter(FDAF), the Multi-Delay Adaptive Filter (MDAF), the Windowing FrequencyDomain Adaptive Filter (WDAF), etc. A term for updating a parameter ofthe first stage adaptive filter in frequency domain is ΔH_(0,t), whereΔH_(0,t) is a N-order vector with N a positive integer.

The echo path model H_(0,t+1) of the first stage adaptive filtercorresponding to the downlink reference signal x(t) is updated as:H_(0,t+1)=H_(0,t)+ΔH_(0,t), where ΔH_(0,t) is a N-order vector with N apositive integer.

A reason for multiple pre-distortion mapping functions are needed forobtaining the pre-distorted signal is that distortion of a speakerpossesses features of complexity and time-variance, and it is unlikelyfor one distortion process to effectively approach a distortion portionin an echo signal, so that the embodiments of the present disclosureemploy results of different multi-path distortion processes forproviding a plenty of selections to a final minimum fusion.

Specifically, an echo path model in an adaptive filter is either a timedomain model or a frequency domain model. Regarding to these two models,the embodiments of the present disclosure provide following descriptionsabout the at least one second stage adaptive filter corresponding to thepre-distorted signal and the second error signal e_(k)(t).

(1) if an echo path model in the at least one second stage adaptivefilter corresponding to the pre-distorted signal is a time domain modelh_(k,t), the second error signal e_(k)(t) is expressed as:

${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$where h_(k,t) is a k-th M-order FIR filter at a time t,h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose,

denotes convolution, t is the time index and M is the number of order.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency.

If the echo path model is the time domain model h_(k,t)(k=1, 2, . . . ,K), the at least one second stage adaptive filter may employ any onefrom the time domain adaptive filtering algorithms, where the timedomain adaptive filtering algorithms include: the Least Mean Square(LMS), the Normalized Least Mean Square (NMLS), the Affine Projection(AP), the Fast Affine Projection (FAP), the Least Square (LS), theRecursive Least Square (RLS), etc. A term for updating a parameter ofthe at least one second stage adaptive filter in time domain isΔh_(k,t)(k=1, 2, . . . , K), where Δh_(k,t) is a M-order vector with M apositive integer.

The echo path model h_(k,t+1) of the at least one second stage adaptivefilter corresponding to the K-path pre-distorted signal is updated as:h_(k,t+1)=h_(k,t)+Δh_(k,t), where the term for updating the parameter ofthe at least one second stage adaptive filter in time domain isΔh_(k,t), where Δh_(k,t) is a M-order vector with M a positive integer,and k=1, 2, . . . , K.

(2) if the echo path model in the at least one second stage adaptivefilter corresponding to the pre-distorted signal is a frequency domainmodel H_(k,t), the second error signal e_(k)(t) is expressed as:e _(k)(t)=e ₀(t)−y _(k)(t).[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M)×(N−M))]F ⁻[H _(k,t) ·R _(k,t)],where y_(k)(t) is the second stage filtered signal, t is the time index,N is length of a signal frame, M is the number of order, 0_((N−M)×M) isthe (N−M) by M zero matrix, I_((N−M)×(N−M)) is the (N−M) by (N−M)identity matrix, F⁻ is the inverse discrete Fourier transform matrix,H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)=F[r_(k)(t−N+1), r_(k)(t−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. , K, y₁(t), y₂(t), . . . , y_(K)(t) are K-path filtered outputsignals, · denotes the dot product, F is the discrete Fourier transformmatrix, and the superscript T denotes transpose, k=1, 2, . . . , K, andK is a positive integer.

The order M may need to satisfy the simulated echo path model;generally, echo attenuation may last 10 ms to 1 s so that the order Mranges from 0.01 f_(s) to f_(s), where f_(s) is the sampling frequency,and N may need to be greater than M.

If the echo path model is the frequency domain model H_(k,t), the atleast one second stage adaptive filter may employ any one from thefrequency domain adaptive filtering algorithms, where the frequencydomain adaptive filtering algorithms include: the Frequency DomainAdaptive Filter (FDAF), the Multi-Delay Adaptive Filter (MDAF), theWindowing Frequency Domain Adaptive Filter (WDAF), etc. A term forupdating a parameter of the at least one second stage adaptive filter infrequency domain is ΔH_(k,t), where ΔH_(k,t) is a N-order vector with Na positive integer.

The echo path model H_(k,t+1) of the at least one second stage adaptivefilter corresponding to the K-path pre-distorted signal is updated as:H_(k,t+1)=H_(k,t)+ΔH_(k,t), where ΔH_(k,t) is a N-order vector with N apositive integer and k=1, 2, . . . , K.

In order to further improve the aforementioned embodiment, referring toFIG. 5, the embodiment of the present disclosure provide a structuraldiagram for a fusion processing unit, which may include: a mappingsub-unit 51, adapted for mapping the first error signal e₀(t) and thesecond error signal e_(k)(t) (k=1, 2, . . . , K) to correspondingmapping signals respectively using an invertible space mapping method; ametric computing sub-unit 52, adapted for computing metricscorresponding to the mapping signals using a preset minimum metricfunction; a searching sub-unit 53, adapted for searching for a minimummetric from the metrics; a residue signal obtaining sub-unit 54, adaptedfor mapping a mapping signal corresponding to the minimum metric back toa space in which the first error signal e₀(t) and the second errorsignal e_(k)(t) reside, so as to obtain the residue signal e(t).

Regarding to K+1 error signals (including the first error signal e₀(t)and the second error signal e_(k)(t) for k=1, 2, . . . , K, due toadaptive filters have different parameter signals, respective remainingechoes are minimum at different time or in different space. The minimumvalue fusion process may use a spatial mapping method to map the K+1error signals e₀(t), e₁(t), . . . , e_(K)(t) to mapping signals S_(0,t),S_(1,t), . . . , S_(K,t), and the preset minimum metric function may beused to compute the metrics v₀, v₁, . . . , v_(K) corresponding to themapping signals S_(0,t), S_(1,t), . . . , S_(K,t). A minimum metricv_(k) _(_) _(min) is searched among the metrics, and a k_min-th mappingsignal S_(k) _(_) _(min,t) corresponding to the minimum metric v_(k)_(_) _(min) is mapped back to an original space, in which the K+1 errorsignals reside, so as to obtain the residue signal e(t). Eventually, theresidue signal e(t) is regarded as the final output of adaptive echocancellation.

In a simplest minimum value fusion process, the spatial mapping methodis short-time signal framing that:S _(k,t)[e _(k)(t−L+1),e _(k)(t−L+2), . . . ,e _(k)(t)],k=0,1,2, . . .,K,where S_(k,t) is the mapping signal, and t is the time index.

A minimum metric function is used for computing a short-time extentthat:

${v_{k} = {{f_{m\; i\; n}( S_{k,t} )} = {\sum\limits_{l = 1}^{L}{{e_{k}( {t - L + l} )}}}}},{k = 0},1,2,\ldots\mspace{14mu},K,$where v_(k) is the minimum metric, and t is the time index.

Alternatively, a minimum metric function is used for computing ashort-time energy that:

${v_{k} = {{f_{m\; i\; n}( S_{k,t} )} = {\sum\limits_{l = 1}^{L}{e_{k}( {t - L + l} )}}}},{k = 0},1,2,\ldots\mspace{14mu},K,$where v_(k) is the minimum metric, and t is the time index.

In the aforementioned equations, L is expressed as a short-timeinterval, which is a positive integer, and a value of L is between 0.001f_(s) and f_(s), where f_(s) is the sampling frequency.

The mapping signal S_(k) _(_) _(min,t) corresponding to the minimumshort-time extent or the minimum short-time energy is selected, and aset [e_(k) _(_) _(min)(t−L+1), . . . , e _(k) _(_) _(min)(t)]corresponding to the mapping signal S_(k) _(_) _(min,t) is regards as aset of final residue signals [e(t−L+1), . . . , e(t)].

In some embodiments, the short-time interval may be overlapped so as toperform smoothing process to both ends of the short-time interval.

A more effective minimum fusion process may include: a frequency-domaintransform as presented in the following that:S_(k,t)=T_(F)([e_(k)(t−L+1), . . . , e_(k)(t)]), k=0, 1, 2, . . . , K,where S_(k,t) is the mapping signal, T_(F) denotes a frequency-domaintransform, L denotes the short-time interval, which is a positiveinteger, and the value of L is between 0.001 f_(s) and f_(s), wheref_(s) is the sampling frequency.

The frequency-domain transform T_(F) may include but be not limited thatthe Discrete Fourier Transform (DFT), the Discrete Cosine Transform(DCT), the Karhunen-Loeve (KL) transform, the Modified Discrete CosineTransform (MDCT), etc. The frequency-domain transform T_(F) may beinvertible and an inverse transform is expressed as T_(F) ⁻.

The mapping signal S_(k,t) obtained by the frequency-domain transform isa vector of L_(F) elements. For different mapping methods, L_(F) may bedifferent. In cases of the DFT transform, the DCT transform and the KLtransform, L_(F) is generally equal to L, and in a case of the MDCTtransform, L_(F) is equal to L/2. The minimum metric function may be anorm of the mapping signal S_(k,t)[l], l=1, 2, . . . , L _(F):f_(min)(x)=|x|, and alternatively, an addition of a weighted absolutevalue of a real part of a number and a weighted absolute value of animaginary part of the number:f _(min)(x)=λ_(real)|real(x)|^(γ) ^(real) +λ_(imag)|imag(x)|^(γ) ^(imag),where λ_(real) and λ_(imag) are weighting factors, which arenon-negative real numbers, and γ_(real) and γ_(imag) are order numbers,which also are non-negative real numbers.

A metric of the mapping signal S_(k,t)[l], l=1, 2, . . . , L_(F) isobtained using the minimum metric function that:v_(k,l)=f_(min)(S_(k,l)[l]), where v_(k,l) is a minimum metric, aninteger index l=1, 2, . . . , L_(F) and k=0, 1, 2, . . . , K.

Based on the metric, the mapping signal S_(k,l)[l], l=1, 2, . . . ,L_(F) is fused as a fused signal S_(t)[l]=S_(k) _(_) _(l,t)[l], where aninteger index l=1, 2, . . . , L_(F), and k_l satisfies the followingequation that: f_(min)(S_(k) _(l) _(,t)[l])=min([v_(0,l), v_(1,l)]); atlast, performing the inverse frequency-domain transform T_(F) ⁻, thefused signal S_(t)[l], l=1, 2, . . . , L_(F) is inversely mapped toobtain the set of final residue signals that [e(t-L+1), e(t)], where[e(t−L+1), . . . , e(t)]=T_(F) ⁻(S_(t)).

In some embodiments, the short-time interval may be overlapped so as toperform smoothing process to both ends of the short-time interval.

Specifically, in the aforementioned embodiments of the presentdisclosure, K is always a positive integer.

Specifically, for the embodiments of the apparatus for reducing an echo,the working principle of each component refers to the correspondingembodiments of the method for reducing an echo, thus no more repetitionis provided.

Based on the aforementioned description of the embodiments of thepresent disclosure, those skilled in the art can realize and implementthe embodiments of the present disclosure. Modifications to theembodiments of the present disclosure are obvious to those skilled inthe art. Without departing from the spirit or scope of the disclosure,the principles defined in the present disclosure can be applied to otherembodiments. Accordingly, the present disclosure is not limited to theembodiments in the present disclosure but a largest scope in accordancewith the principles and novelties disclosed in the present disclosure.

What is claimed is:
 1. A method for reducing an echo, comprising:invoking a first stage adaptive filter corresponding to a downlinkreference signal x(t), and performing a first filtering process to thedownlink reference signal x(t) so as to obtain a first stage filteredsignal y₀(t); subtracting a target signal by the first stage filteredsignal y₀(t) so as to obtain a first error signal e₀(t); performing aK-path gain process to the downlink reference signal x(t) so as toobtain a K-path pre-processed signal, where K is a positive integer;performing a pre-distortion process to the K-path pre-processed signalso as to obtain a corresponding K-path pre-distorted signal r_(k)(t)(k=1, 2, . . . , K); invoking at least one second stage adaptive filtercorresponding to the K-path pre-distorted signal, and performing asecond filtering process to the K-path pre-distorted signal so as toobtain a corresponding K-path second stage filtered signal y_(k)(t);subtracting the first error signal e₀(t) by the K-path second stagefiltered signal y_(k)(t) so as to obtain a second error signal e_(k)(t),wherein k=1, 2, . . . , K; performing a minimum value fusion process tothe first error signal e₀(t) and the second error signal e_(k)(t) so asto obtain a residue signal e(t); and considering the residue signal e(t)as a final output of adaptive echo cancellation.
 2. The method accordingto claim 1, wherein, if an echo path model in the first stage adaptivefilter corresponding to the downlink reference signal x(t) is a timedomain model h_(0,t), the first error signal e₀(t) is expressed as:${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$wherein d(t) is a target signal, y₀(t) is the first stage filteredsignal, h_(0,t) is a M-order FIR filter at time t, h=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex, M is a number of order, h_(0,t) is a filter coefficient of theM-order FIR filter h_(0,t) at time t, and m denotes an integer indexincreasing from 1 to M.
 3. The method according to claim 2, wherein, ifthe echo path model is a time domain model h_(0,t), the echo path modelh_(0,t+1) of the first stage adaptive filter corresponding to thedownlink reference signal x(t) is updated as:h _(0,t+t) =h _(0,t) +Δh _(0,t) wherein h_(0,t+1) is a M-order FIRfilter at time t+1, Δh_(0,t) is a term for updating a parameter of thefirst stage adaptive filter in time domain, where Δh_(0,t) is a M-ordervector with M a positive integer.
 4. The method according to claim 1,wherein, if an echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a frequencydomain model H_(0,t), the first error signal e₀(t) is expressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(t)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)], wherein d(t) is the targetsignal, y₀(t) is the first stage filtered signal, t is a time index, asuperscript T denotes transpose, N is length of a signal frame, M is anumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix, I_((N−M)×M) isa (N−M) by (N−M) identity matrix, F⁻ is an inverse discrete Fouriertransform matrix, H_(0,t) denotes a N-order vector at the time t, andR_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T).
 5. The method accordingto claim 4, wherein, if the echo path model is a frequency domain modelH_(0,t), the echo path model H_(0,t+1) of the first stage adaptivefilter corresponding to the downlink reference signal x(t) is updatedas:H _(0,t+1) =H _(0,t) +ΔH _(0,t), wherein H_(0,t+1) denotes a N-ordervector at the time t+1, ΔH_(0,t) is a term for updating a parameter ofthe first stage adaptive filter in frequency domain, where ΔH_(0,t) is aN-order vector with N a positive integer.
 6. The method according toclaim 1, wherein the pre-distortion mapping function employed by thepre-distortion process is expressed as:r _(k)(t)=f _(k)(p _(k)(t)), wherein r_(k)(t) is the k-th pre-distortedsignal, p_(k)(t) is the k-th pre-processed signal, f_(k) (x)≠cx, f_(k)(x)≠c, x denotes p_(k)(t), f_(k)(x) denotes the pre-distortion mappingfunction, c is a constant, and k=1, 2, . . . , K.
 7. The methodaccording to claim 1, wherein, if an echo path model in the at least onesecond stage adaptive filter corresponding to the K-path pre-distortedsignal is a time domain model h_(k,t), the second error signal isexpressed as:${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$wherein h_(k,t) is a k-th M-order FIR filter at a time t, k=1, 2, . . ., K, h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose, denotes convolution, t is a time index,M is a number of order, and m denotes an integer index increasing from 1to M.
 8. The method according to claim 7, wherein, if the echo pathmodel is a time domain model h_(k,t), the echo path model h_(k,t+1) ofthe at least one second stage adaptive filter corresponding to theK-path pre-distorted signal is updated as:h _(k,t+1) =h _(k,t) +Δh _(k,t), wherein h_(k,t+1) is a k-th M-order FIRfilter at a time t+1, Δh_(k,t) is the term for updating the parameter ofthe at least one second stage adaptive filter in time domain, whereΔh_(k,t) is a M-order vector with M a positive integer, and k=1, 2, . .. , K.
 9. The method according to claim 1, wherein, if an echo pathmodel in the at least one second stage adaptive filter corresponding tothe K-path pre-distorted signal is a frequency domain model H_(k,t), thesecond error signal e_(k)(t) is expressed as:e _(k)(t)=e ₀(t)−

,[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M)×(N−M))]F ⁻[H _(k,t) ·R _(k,t)], wherein y_(k)(t) is the K-pathsecond stage filtered signal, t is a time index, a superscript T denotestranspose, N is length of a signal frame, M is a number of order,0_((N−M)×M) is a (N−M) by M zero matrix, I_((N−M)×(N−M)) is a (N−M) by(N−M) identity matrix, F⁻ is an inverse discrete Fourier transformmatrix, H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)=F[r_(k)(t−N+1), r_(k)(t−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. , K.
 10. The method according to claim 9, wherein, if the echo pathmodel is a frequency domain model H_(k,t), the echo path model H_(k,t+1)of the at least one second stage adaptive filter corresponding to thepre-distorted signal is updated as:H _(k,t+1) =H _(k,t) +ΔH _(k,t), wherein H_(k,t+1) denotes a k-thN-order vector at the time t+1, ΔH_(k,t) is a term for updating aparameter of the at least one second stage adaptive filter in frequencydomain, ΔH_(k,t) is a N-order vector, and N is a positive integer. 11.The method according to claim 1, wherein performing a minimum valuefusion process to the first error signal e₀(t) and the second errorsignal e_(k)(t) (k=1, 2, . . . , K) so as to obtain a residue signal,comprising: mapping the first error signal e₀(t) and the second errorsignal e_(k)(t) (k=1, 2, . . . , K) to corresponding mapping signalsrespectively using an invertible space mapping method; computing metricscorresponding to the mapping signals using a preset minimum metricfunction; searching for a minimum metric from the metrics; and mapping amapping signal corresponding to the minimum metric back to a space inwhich the first error signal e₀(t) and the second error signal e_(k)(t)reside, so as to obtain the residue signal e(t).
 12. An apparatus forreducing an echo, comprising: a first stage filtering unit, configuredfor invoking a first stage adaptive filter corresponding to a downlinkreference signal x(t), and performing a first filtering process to thedownlink reference signal x(t) so as to obtain a first stage filteredsignal y₀(t); a first subtracting unit, configured for subtracting atarget signal by the first stage filtered signal y₀(t) so as to obtain afirst error signal e₀(t); a gain unit, configured for performing aK-path gain process to the downlink reference signal x(t) so as toobtain a K-path pre-processed signal, where K is a positive integer; apre-distortion processing unit, configured for performing apre-distortion process to the K-path pre-processed signal so as toobtain a corresponding K-path pre-distorted signal r_(k)(t) (k=1, 2, . .. , K); a second stage filtering unit, configured for invoking at leastone second stage adaptive filter corresponding to the K-pathpre-distorted signal, and performing a second filtering process to theK-path pre-distorted signal so as to obtain a corresponding K-pathsecond stage filtered signal y_(k)(t); a second subtracting unit,configured for subtracting the first error signal e₀(t) by the K-pathsecond stage filtered signal y_(k)(t) so as to obtain a second errorsignal e_(k)(t), wherein k=1, 2, . . . , K; a fusion processing unit,configured for performing a minimum value fusion process to the firsterror signal e₀(t) and the second error signal e_(k)(t) so as to obtaina residue signal e(t); and an output unit, configured for consideringthe residue signal e(t) as a final output of adaptive echo cancellation.13. The apparatus according to claim 12, wherein, if an echo path modelin the first stage adaptive filter corresponding to the downlinkreference signal x(t) is a time domain model h_(0,t), the first errorsignal e₀(t) is expressed as:${{e_{0}(t)} = {{d(t)} - {y_{0}(t)}}},{{y_{0}(t)} = {{h_{0,t} \otimes {x(t)}} = {\sum\limits_{m = 1}^{M}{{h_{0,t}(m)}{x( {t - M + m} )}}}}},$wherein d(t) is a target signal, y₀(t) is the first stage filteredsignal, h_(0,t) is a M-order FIR filter at time t, h_(0,t)=[h_(0,t)(1),h_(0,t)(2), . . . , h_(0,t)(M)]^(T), a superscript T denotes transpose,

denotes convolution, x(t) is the downlink reference signal, t is a timeindex, M is a number of order, h_(0,t) is a filter coefficient of theM-order FIR filter h_(0,t) at time t, and m denotes an integer indexincreasing from 1 to M.
 14. The apparatus according to claim 13,wherein, if the echo path model is a time domain model h_(0,t), the echopath model h_(0,t+1) of the first stage adaptive filter corresponding tothe downlink reference signal x(t) is updated as:h _(0,t−1) =h _(0,t) °Δh _(0,t), wherein h_(0,t+1) is a M-order FIRfilter at time t+1, Δh_(0,t) is a term for updating a parameter of thefirst stage adaptive filter in time domain, where Δh_(0,t) is a M-ordervector with M a positive integer.
 15. The apparatus according to claim12, wherein, if an echo path model in the first stage adaptive filtercorresponding to the downlink reference signal x(t) is a frequencydomain model H_(0,t), the first error signal e₀(t) is expressed as:e ₀(t)=d(t)−y ₀(t),[y ₀(t−(N−M)+1),y ₀(t−(N−M)+2), . . . ,y ₀(t)]^(T)=[0_((N−M)×M) I_((N−M)×(N−M))]F ⁻[H _(0,t) ·R _(0,t)], wherein d(t) is a target signal,y₀(t) is the first stage filtered signal, t is a time index, asuperscript T denotes transpose, N is length of a signal frame, M is anumber of order, 0_((N−M)×M) is a (N−M) by M zero matrix,I_((N−M)×(N−M)) is a (N−M) by (N−M) identity matrix, F⁻ is an inversediscrete Fourier transform matrix, H_(0,t) denotes a N-order vector atthe time t, and R_(0,t)=F[x(t−N+1), x(t−N+2), . . . , x(t)]^(T).
 16. Theapparatus according to claim 15, wherein, if the echo path model is afrequency domain model H_(0,t), the echo path model H_(0,t+1) of thefirst stage adaptive filter corresponding to the downlink referencesignal x(t) is updated as:H _(0,t+1) =H _(0,t) +ΔH _(0,t), wherein H_(0,t+1) denotes a N-ordervector at the time t+1, ΔH_(0,t) is a term for updating a parameter ofthe first stage adaptive filter in frequency domain, where ΔH_(0,t) is aN-order vector with N a positive integer.
 17. The apparatus according toclaim 12, wherein the pre-distortion mapping function employed by thepre-distortion process is expressed as:r _(k)(t)=f _(k)(p _(k)(t)), wherein r_(k)(t) is the k-th pre-distortedsignal, p_(k)(t) is the k-th pre-processed signal, f_(k) (x)≠cx,f_(k)(x)≠c, x denotes p_(k)(t), f_(k)(x) denotes the pre-distortionmapping function, c is a constant, and k=1, 2, . . . , K.
 18. Theapparatus according to claim 12, wherein, if an echo path model in theat least one second stage adaptive filter corresponding to thepre-distorted signal is a time domain model h_(k,t), the second errorsignal is expressed as:${{e_{k}(t)} = {{e_{0}(t)} - {y_{k}(t)}}},{{y_{k}(t)} = {{h_{k,t} \otimes {r_{k}(t)}} = {\sum\limits_{m = 1}^{M}{{h_{k,t}(m)}{r_{k}( {t - M + m} )}}}}},$wherein h_(k,t) is a k-th M-order FIR filter at a time t, k=1, 2, . . ., K, h_(k,t)=[h_(k,t)(1), h_(k,t)(2), . . . , h_(k,t)(M)]^(T), thesuperscript T denotes transpose, denotes convolution, t is a time index,M is a number of order, and m denotes an integer index increasing from 1to M.
 19. The apparatus according to claim 18, wherein, if the echo pathmodel is a time domain model h_(k,t), the echo path model h_(k,t+1) ofthe at least one second stage adaptive filter corresponding to thepre-distorted signal is updated as:h _(k,t+1) =h _(k,t) +Δh _(k,t), wherein h_(k,t+1) is a k-th M-order FIRfilter at a time t+1, Δh_(k,t) is the term for updating the parameter ofthe at least one second stage adaptive filter in time domain, whereΔh_(k,t) is a M-order vector with M a positive integer, and k=1, 2, . .. , K.
 20. The apparatus according to claim 12, wherein, if an echo pathmodel in the at least one second stage adaptive filter corresponding tothe K-path pre-distorted signal is a frequency domain model H_(kt), thesecond error signal e_(k)(t) is expressed as:e _(k)(t)=e ₀(t)−

,[y _(k)(t−(N−M)+1),y _(k)(t−(N−M)+2), . . . ,y _(k)(t)]^(T)=[0_((N−M)×M)I _((N−M)×(N−M))]F ⁻[H _(k,t) ·R _(k,t)], wherein y_(k)(t) is the K-pathsecond stage filtered signal, t is a time index, a superscript T denotestranspose, N is length of a signal frame, M is a number of order,0_((N−M)×M) is a (N−M) by M zero matrix, I_((N−M)×(N−M)) is a (N−M) by(N−M) identity matrix, F⁻ is an inverse discrete Fourier transformmatrix, H_(k,t) denotes a k-th N-order vector at the time t,R_(k,t)F[r_(k)(t−N+1), r_(k)(t−N+2), . . . , r_(k)(t)]^(T), k=1, 2, . .. , K.
 21. The apparatus according to claim 20, wherein, if the echopath model is a frequency domain model H_(k,t), the echo path modelH_(k,t+1) of the at least one second stage adaptive filter correspondingto the pre-distorted signal is updated as:H _(k,t+1) =H _(k,t) +ΔH _(k,t), wherein H_(k,t+1) denotes a k-thN-order vector at the time t+1, ΔH_(k,t) is a term for updating aparameter of the at least one second stage adaptive filter in frequencydomain, ΔH_(k,t) is a N-order vector, and N is a positive integer. 22.The apparatus according to claim 12, wherein the fusion processing unitcomprises: a mapping sub-unit, configured for mapping the first errorsignal e₀(t) and the second error signal e_(k)(t) (k=1, 2, . . . , K) tocorresponding mapping signals respectively using an invertible spacemapping method; a metric computing sub-unit, configured for computingmetrics corresponding to the mapping signals using a preset minimummetric function; a searching sub-unit, configured for searching for aminimum metric from the metrics; and a residue signal obtainingsub-unit, configured for mapping a mapping signal corresponding to theminimum metric back to a space in which the first error signal e₀(t) andthe second error signal e_(k)(t) reside, so as to obtain the residuesignal e(t).